About the Job
At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.
Lyft’s Data Science Team builds mathematical models underpinning the platform’s core services. Compared to other technology companies of a similar size, the set of problems that we tackle is incredibly diverse. They cut across optimization, prediction, modeling, inference, transportation, and mapping. We're looking ...
We are hiring for a variety of Data Science interns, focusing on the following specialties:
Key Responsibilities
Partner with Engineers, Product Managers, and other cross-functional partners to frame problems, both mathematically and within the business context
Perform exploratory data analysis to gain a deeper understanding of the problem
Write production modeling code; collaborate with software engineers to implement algorithms in production
Design and run both simulated and live traffic experiments
Analyze experimental and observational data; communicate findings including working with partner teams and presentations; facilitate launch decisions
Currently pursuing a Masters or PhD degree in mathematical sciences (Operations Research, Computer Science, Statistics, Applied Mathematics, Theoretical Physics, Behavioral Science, Electrical Engineering, etc.), Economics (Microeconomics Theory, Econometrics etc.), Data Engineering; or a related field; with a graduation date between December 2027 and June 2028 (required)
Available during Summer 2027 for an internship in New York City
Experience coding in Python (required) or SQL, R; standard data science libraries (NumPy, Scikit-learn, PyTorch, TensorFlow, Keras); and ML Tools & Libraries (NumPy, SpaCy, NLTK, Scikit-learn, TensorFlow, Keras)
Experimental design and analysis
Exploratory data analysis
Expertise in one of these specialties: optimization and mathematical modeling, machine learning fundamentals, or probabilistic and statistical modeling
Bonus points: Experience in marketplace design, ridesharing, studying two-sided marketplaces, and/or transportation
